Current methodologies of tracking consumer patterns commonly rely upon patterns based on similarity between users. For example, when several users like a video on social media or purchase the same product, then other users may be likely to enjoy the same video or buy the same product. Similar patterns of purchasing across different payment types may also help in learning consumers' habits.
Some conventional approaches collect data from groups of individuals in order to understand common behaviors. These approaches use statistical confidence and modeling to predict future acts. Approaches using groups of individuals also use machine learning techniques to predict future purchases. Machine learning mechanisms used may include artificial neural networks, inductive logic programming, and deep learning. Statistical methods and predictions are also commonly relied upon during predicting behavior.
Some current methodologies are limited to group behavior patterns. Group behavior patterns lack the specificity that indicates whether a user will make a purchase or will perform a specific behavior in real-time. For example, some techniques use one or more variables to track different purchases over time within a category. For example, these techniques attempt to track all shoppers at a certain grocery store within several variables. The variables may indicate different product groups, different individual products, and different groups of shoppers.
Each individual may make purchases at different frequencies. For example, shoppers may purchase at a frequency depending on a particular shopper's lifestyle. One person living in the middle of a city may purchase food every few days from a grocery store. Another person living in a rural area may shop less frequently, such as once a month. Shopping habits for clothing may similarly differ by shopper. One person might buy clothes every week, while another person may purchase clothing once a year.
In a group learning example, a grocery store may accumulate data on all customer purchases for different categories. The store may organize the data by product category, such as produce, deli, health care, cleaning supplies, etc. Similarly, the store may organize the data by individual products, such as toothpaste, waffles, chicken, and laundry detergent. Many consumer tracking mechanisms use data that aggregates data from several customers. Therefore, these methods cannot easily predict what individual customers want and when they will want it. Thus, there is a need to more accurately predict individual user's needs in a timely manner.